Graduating computer science and software engineering students do not always possess the necessary skills, abilities, or knowledge when beginning their careers in the software industry. The lack of these skills and abilities can limit the productivity of newly hired, recent graduates, or even prevent them from gaining employment. This paper presents the results of an empirical study where twenty-three managers and hiring personnel from various software companies in the United States and Europe were interviewed. Participants were asked about areas where recent graduates frequently struggled when beginning employment at their companies and which skill deficiencies might prevent a recent graduate from being hired. The results of this study indicate that recent graduates struggle with using configuration management systems (and other software tools), effectively communicating with co-workers and customers, producing unit tests for their code, and other skills or abilities. The results also indicate that a lack of project experience and problem solving abilities are the most commonly cited issues preventing students from gaining employment. This research is intended to assist educators in identifying areas where students may not measure up the expectations of industry companies and in improving the curriculum at their universities to better prepare them for their future careers.
Pair Programming has been shown to be beneficial to student learning. Much research has been conducted to effectively create student pairs when using pair programming in introductory computer science courses. This paper reports results of research investigating the effectiveness of pairing students based on their mental model consistency. Prior research has found a strong correlation between mental model consistency and performance in introductory computer programming courses. Evaluating students' mental models helps to provide insights into how students approach problem solving and may indicate how to effectively pair students to improve their programming ability and learning. The results from an empirical study conducted to investigate these effects indicate that mental model consistency is a predictor of student success in an introductory programming course. Future goals of this research are to fully evaluate all possible pairing arrangements and to produce tests that can be used to evaluate mental model consistency for other computer science concepts.
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